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October 06, 2024

Machine learning and Deep Learning in Economics

Author:




Artur Haponik

CEO & Co-Founder


Reading time:




17 minutes


Although machine learning (ML) continues to gain interest among economists, there is still a lack of practical information about what it entails, what makes it different from classical econometrics, and, finally, how economists and businesses can best use it. Let’s examine current knowledge and see how machine learning is used in economics. It will lead us to one conclusion – machine learning in economics will keep growing rapidly and its impact on the market will soon become fundamental.

Machine learning is the bedrock of market-proven automation, driving real-life value with precision and consistency. While generative AI captivates headlines with creativity, it’s the solid backbone of machine learning that quietly powers sustainable, scalable solutions behind the scenes.

– Edwin Lisowski, COO and co-founder at Addepto.

Machine_ Learning_CTA

 

One of the companies that conducts research on how Artificial Intelligence and machine learning services can influence economics is PWC UK, one of the leading consulting companies in the world.

In their report, called “The economic impact of artificial intelligence on the UK economy” that was published in June 2017 we can read that “UK GDP will be up to 10.3% higher in 2030 as a result of AI – making it one of the biggest commercial opportunities in today’s fast-changing economy. The impact over the period will come from productivity gains (1.9%) and consumption-side product enhancements and new firm entry stimulating demand (8.4%)”.

Artificial Intelligence used to measure economy

So, according to PWC, Artificial Intelligence, along with machine learning can contribute considerably to economic growth in three main areas:

  • Improvement of productivity
  • Product enhancement
  • Stimulating new companies

These three areas are essential for economics and market development in general. You can judge, just by looking at them, that machine learning in economics will have a massive impact on the market’s and society’s development and, in fact, the pace of that development as well. Machine learning will be a necessity for every new company entering the market.

Machine learning vs deep learning

Machine learning (ML) and deep learning (DL) are both subsets of artificial intelligence, but they differ significantly in their methodologies, complexity, and applications.

Here’s a detailed comparison between machine learning and deep learning:

Definition and structure

  • Machine Learning
    ML encompasses a broad range of algorithms that allow computers to learn from data without being explicitly programmed. It can handle various types of tasks, including classification, regression, and clustering, using simpler models like decision trees or linear regression.
  • Deep Learning
    DL is a specialized subset of ML that utilizes artificial neural networks (ANNs) with multiple layers to process data. This structure mimics the human brain’s neural network, enabling DL to automatically identify intricate patterns in large datasets.

Data Requirements

  • Machine Learning
    Typically requires smaller datasets to train effectively. It can perform well with thousands of data points but depends heavily on the quality of the data.
  • Deep Learning
    Requires vast amounts of data—often millions of data points—to achieve high accuracy. The more data it processes, the better it becomes at recognizing complex patterns.

Complexity and computation

  • Machine Learning
    Generally involves simpler algorithms that can be executed on standard computers.
  • Deep Learning
    Involves complex architectures that require substantial computational power, often utilizing GPUs for training.

Applications

  • Machine Learning
    Suitable for a wide range of applications, including straightforward tasks like spam detection or recommendation systems. It excels in scenarios where interpretability and speed are crucial.
  • Deep Learning
    Best suited for more complex tasks such as image recognition, natural language processing, and autonomous systems. Its ability to analyze unstructured data makes it particularly powerful for applications requiring high levels of abstraction.

In summary, while both machine learning and deep learning aim to leverage data for predictive analytics, deep learning represents a more advanced approach that requires more data and computational resources but offers greater capabilities for handling complex tasks.

Interested in machine learning?
Read our article: Machine Learning. What it is and why it is essential to business?

What is common with machine learning and economics?

Well, the shortest and obvious answer is that machine learning and economics are based on data. We have two approaches: traditional, which is econometrics and innovative, which is machine learning. Both of them have a lot of overlap. Econometrics is basically statistics geared towards answering economic questions. Machine learning in economics has a similar purpose but with the usage of huge amount of data. Also, machine learning in economics is not based on exactly the same models as econometrics.

men working on a computer using code

So, we can say that econometrics and machine learning are just two different roads to the same destination. But those roads are quite different. As Paul A. Samuelson and William D. Nordhaus have written in their book Economy – econometrics are allowing economists “to sift through mountains of data to extract simple relationships”. Applied econometrics uses real-world data for assessing economic theories, developing econometric models, analyzing economic history, and forecasting. All of that is done by econometricians with the usage of certain models.

Here are the key commonalities and contrasts between the two fields:

  • Data Utilization
    Both fields leverage large datasets to derive insights. Machine learning excels in processing vast amounts of data, identifying patterns, and making predictions, which is increasingly relevant in economics as data becomes more abundant and complex[1][4]. Economists are beginning to adopt ML techniques to enhance their analyses, particularly in areas like forecasting economic indicators and evaluating policy impacts.
  • Complexity Handling
    Traditional econometric models often struggle with non-linear relationships and high-dimensional data. Machine learning methods can capture these complexities more effectively, allowing economists to explore new dimensions of economic phenomena. For instance, ML can analyze unstructured data such as text or images, which traditional methods might overlook.
  • Predictive vs. Causal Focus
    A fundamental distinction lies in their objectives. Economics traditionally emphasizes causal inference—understanding how changes in one variable affect another. In contrast, machine learning focuses on prediction accuracy without necessarily establishing causal links[2][3]. This difference means that while ML can enhance predictive power in economic models, it may not address the causal questions that are central to economic theory.
  • Model Selection
    Economists typically select models based on theoretical foundations and then estimate them, while ML approaches often automate model selection through algorithms that evaluate numerous potential models simultaneously. This allows for a more systematic exploration of possible relationships within the data.

The convergence of machine learning and economics represents a significant evolution in how economic research is conducted. While both fields have distinct focuses—prediction for ML and causality for economics—their integration offers powerful tools for analyzing complex economic systems and improving policy effectiveness. As this relationship continues to develop, it is likely to reshape both academic research and practical applications in the field of economics.

Use of Machine Learning in Economics

On the other hand, we have machine learning with all its benefits. Machine learning algorithms are capable of analyzing hundreds of millions of bytes in order to find correlations, connections, and even predictions. Some of them are very difficult to spot without machine learning algorithms. As you already know, machine learning applications and algorithms are much faster, more accurate and effective in their work than human scientists. All they need for their job is big data they can base on. Therefore, machine learning in economics reaches a level that is absolutely out of range for standard, traditional econometrics.

code

But that doesn’t mean econometrics and machine learning exclude themselves! Stanford University in one of the studies predicts “development of new econometric methods based on machine learning designed to solve traditional social science estimation tasks”*. So what that means, we can expect synergy of both disciplines. Machine learning and economics (econometrics to be exact), will take the advantages of another in order to create the most efficient predictive method. So both are needed: econometrics and machine learning in economics.

And with that, we go back to the main topic – how machine (and deep) learning is used in economics?

Improvement of productivity

According to PWC, machine learning in economics can increase productivity by up to 14.3% by 2030. Machine learning is a catalyst for productivity growth. In the near future, many current jobs and tasks will be performed totally by machine learning and Artificial Intelligence algorithms or with usage of them. Just think about such jobs as factory workers, cleaning crews, cashiers (even now in more and more shops there are self-service checkouts!), guides (audio guides are already on the market), receptionists, tourist information workers and hundreds more. These jobs are considered simple, and such tasks can easily be performed by machine learning and Artificial Intelligence algorithms, apps and devices.

Artificial Intelligence algorithms man typing

And these professions that will still require human presence will base increasingly on machine learning and Artificial Intelligence. We can predict that one of the key skills of the future worker will be the knowledge of how to co-operate with the Artificial Intelligence algorithms in his or her work. Another job of the not-too-distant-future will be a machine learning specialist and a big data scientist. We will look closer to that subject in one of the following posts.

Demand for big data scientists and machine learning specialists

Big data and machine learning are demanding for new specialists and scientists. And that demand grows rapidly. For instance, as Indeed.com shows we can observe a 29% increase in demand for data scientists year over year and an almost unimaginable 344% increase since 2013.

We will tackle that subject much more in one of the following articles, now just the essence. If you’re after job with perspectives for the future – go for big data, there will be plenty of work for you in the coming years.

“The World Economic Forum’s 2018 Future of Jobs Report” surveyed more than 300 of the world’s largest companies and 85% of them said they wanted to expand big data analytics by 2022**. And what about machine learning specialists? As another report shows, in 2018 there were about 3000 people with skills and background in Artificial Intelligence and in the US itself, there was demand for more than 9000 specialists.

So you clearly see the impact of the big data and therefore machine learning in economics. Future markets will be overfilled with it.

Product enhancement

Thanks to the economics machine learning, current, and future products are and will be better and better tailored to the market’s expectations. Why do we say so? Machine learning can help in increasing the quality of products and services, but also in giving more personalized products and varieties of them to the customers. What’s more, new companies entering the market are able to measure customer’s demand for certain products with amazing precision.

Machine learning in economics can analyze tons of data necessary to make the right business decisions regarding introducing a new product to the market or changing existing ones. Even right now every serious company conducts lots of surveys and studies before making even the smallest change to the product. Doesn’t matter if we talk about packaging, taste, size, price or any other factor. Everything just has to be examined as thoroughly as possible. With the development of economics machine learning, that trend will go sky-high. Imagine machine learning systems doing all the surveys and analytics for the big corporations. Everything would be so much quicker and more accurate.

Machine learning algorithms will execute hundreds of surveys, “talk” with thousands of people worldwide and analyze all data available in order to deliver 100% effective product demanded by the market. And all of that at the same time! Currently, it takes a lot of time to gather proper candidates for the survey, execute it and write a summary. And then you need to analyze and combine data from several countries where surveys had taken place. That takes weeks and months to finish the whole process. Machine learning can shorten it to just days or even less.

measure customer’s demand on a customer shopping

Based on that, we can predict that the product of the future will meet our expectations and requirements much better than it happens now.

Forecasts and predictions

When it comes to prediction, standard econometric models tend to “over-fit” samples and therefore the outcome might be misleading. Machine learning algorithms are much more accurate and deprived of human opinions and judgments. In traditional econometrics, the more complex the model you are basing on is, the higher is the variance and the lower is the bias. So you might expect forecasting error, sometimes smaller, sometimes larger, but it always is there.

This is the point where machine learning in economics comes in. Machine learning algorithms can minimize forecasting error and do the forecast much faster and with the usage of more data. What’s more, machine learning algorithms can analyze many alternative models at the same time, when in traditional econometrics you can analyze just one model at a time.

How does that help? Economics will be a much more precise discipline of knowledge and companies and other organizations will be more encouraged to use it in their work. Just take the economic predictions. What would you do if you had the ability to predict the financial crisis? Or if you could predict the outcome of the elections? Or if you could find out what technology or service will be needed in two years?

Benefits of Machine Learning in Economics

Machine learning (ML) is revolutionizing economics by enhancing data analysis and improving decision-making. Here are the key benefits:

  • Enhanced data analysis

ML excels at processing large datasets, uncovering insights and patterns that traditional methods may miss.

  • Improved forecasting accuracy

ML algorithms can predict economic indicators like GDP growth and inflation rates with greater precision, leading to more reliable forecasts.

  • Automation

By automating routine data collection and analysis, ML allows economists to focus on complex issues, increasing productivity.

  • Risk management

ML helps identify potential economic threats by analyzing trends, enabling proactive risk mitigation strategies.

  • Better financial decision-making

Economists can leverage ML to analyze market data, uncover trends, and make informed investment decisions.

  • Causal inference

ML assists in establishing causal relationships between variables, crucial for effective policy development.

Challenges of Machine Learning in Economics

While machine learning (ML) offers significant potential in economics, it also faces several challenges:

  • Interpretability

ML models can be complex and difficult to interpret compared to traditional econometric models. This lack of transparency makes it challenging for economists to understand the underlying processes that lead to predictions, which can hinder trust and usability in decision-making contexts.

  • Overfitting

The risk of overfitting is prevalent in ML, where models become too tailored to training data, leading to poor performance on new data. This can result in inaccurate predictions and unreliable economic insights.

  • Data quality and availability

High-quality data is crucial for effective ML applications. However, issues such as incomplete or biased datasets can significantly limit the performance of ML models. Additionally, access to relevant data may be restricted due to bureaucratic silos or regulatory barriers.

  • Ethical and fairness concerns

ML models may inadvertently perpetuate biases present in training data, leading to unfair outcomes. Addressing these ethical concerns is essential for ensuring that ML applications contribute positively to social welfare.

  • Computational resources

ML requires substantial computational resources, which can be a barrier for some researchers and institutions, limiting their ability to leverage these advanced techniques effectively.

Applications of Deep Learning in Economics

Deep learning is increasingly applied in economics, significantly enhancing forecasting accuracy and decision-making processes. By analyzing complex, nonlinear relationships in data, deep learning models can effectively predict economic indicators such as GDP growth and inflation. They excel in tasks like multi-country GDP prediction and risk management, identifying patterns that indicate financial instability. Additionally, deep learning automates feature extraction from raw data, improving model performance and adaptability.

Moreover, the integration of deep learning with other machine learning techniques has led to the development of hybrid models that further enhance prediction accuracy. As these technologies evolve, they are set to play a crucial role in economic analysis and policy formulation, providing valuable insights for economists and policymakers alike.

Challenges and opportunities of using ML & DL in economics

In general, this is what economics machine learning is about. To help enhance products and services, improve productivity, and predict the future by giving trustworthy forecasts about economics, market, society, politics, or technology. But for a change, these predictions actually CAN be trustworthy.

programming

Current predictions are mostly based on what someone thinks, whether it’s a one-person or a company. It’s not a reliable source. Forecasts of the future will be based on big data. Machine learning algorithms will analyze the tenths of thousands of gigabytes of data in order to find the most probable outcome or trend. It will no longer be based on “reading tea leaves” so we might expect that its accuracy will be considerably higher.

And as we mentioned earlier – a synergy of machine learning in economics and econometrics can lead to much more accurate models, combining the ability to analyze huge amounts of data and traditional modeling.

Machine Learning and Deep Learning in Economics – FAQ

How is machine learning applied in economic research?

Machine learning techniques are utilized for predictive modeling, analyzing large datasets, and extracting insights from unstructured data. They help economists forecast economic indicators, optimize resource allocation, and improve causal inference by identifying patterns that traditional econometric methods might overlook.

What are the benefits of using deep learning for economics studies?

Deep learning offers enhanced predictive accuracy by capturing complex relationships within large datasets. It automates feature extraction, reducing the need for manual input, and is particularly effective for tasks involving unstructured data, such as sentiment analysis or image recognition related to economic indicators.

What challenges do economists face when integrating machine learning and deep learning into their work?

Key challenges include data quality issues, interpretability of models, and the risk of overfitting. Additionally, while ML methods focus on prediction, they may not adequately address causal relationships that are central to economic theory.

How does machine learning improve forecasting in economics?

Machine learning models minimize forecasting errors by balancing bias and variance, allowing them to generalize better to new data compared to traditional econometric models. This capability is crucial for anticipating market trends and assessing the impact of policy changes.

Can machine learning replace traditional econometric methods?

While machine learning provides powerful tools for analysis and prediction, it does not replace traditional econometric methods. Instead, it complements them by offering new ways to analyze complex datasets and improve empirical research methodologies.

What are some specific applications of deep learning for economics research?

Deep learning is applied in various areas, such as predicting consumer behavior, analyzing financial markets, developing measures of inflation, and optimizing policy decisions through real-time data analysis.

What future trends can we expect in the use of machine learning and deep learning in economics?

Future trends may include increased collaboration between economists and data scientists, the development of hybrid models that combine ML with traditional econometric approaches, and a growing emphasis on ethical considerations in predictive modeling.

If you are interested in applying economics machine learning to your company – we are always keen to talk about that. Our vast experience and know-how ensure that together we will find the best solution for you. Just drop us a line or call us, apply machine learning in economics to your company and get ahead!

The article is an updated version of the publication from Oct. 18, 2029.  



Category:


Machine Learning